Jonas Spinner

Lorentz Local Canonicalization (LLoCa) ensures exact Lorentz-equivariance for arbitrary neural networks with minimal computational overhead. For the LHC, it equivariantly predicts local reference frames for each particle and propagates any-order tensorial information between them. We apply it to graph networks and transformers. We showcase its cutting-edge performance on amplitude regression, end-to-end event generation, and jet tagging. For jet tagging, we introduce a large top tagging dataset to benchmark LLoCa versions of a range of established benchmark architectures and highlight the importance of symmetry breaking.

Generative networks are perfect tools to enhance the speed and precision of LHC simulations. Especially when generating events beyond the size of the training dataset, it is important to understand their statistical precision. We present two complementary methods to estimate the amplification factor without large holdout datasets. Averaging amplification uses Bayesian networks or ensembling to estimate amplification from the precision of integrals over given phase-space volumes. Differential amplification uses hypothesis testing to quantify amplification without any resolution loss. Applied to state-of-the-art event generators, both methods indicate that amplification is already possible in specific regions of phase space.

Generative networks are an exciting tool for fast LHC event generation with fixed number of particles. Autoregressive transformers allow us to generate events containing variable numbers of particles, very much in line with the physics of QCD jet radiation, and offer the possibility to generalize to higher multiplicities. We show how transformers can learn a factorized likelihood for jet radiation and extrapolate in terms of the number of generated jets. For this extrapolation, bootstrapping training data and training with modifications of the likelihood loss can be used.

We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Collider. L-GATr represents data in a geometric algebra over space-time and is equivariant under Lorentz transformations. The underlying architecture is a versatile and scalable transformer, which is able to break symmetries if needed. We demonstrate the power of L-GATr for amplitude regression and jet classification, and then benchmark it as the first Lorentz-equivariant generative network. For all three LHC tasks, we find significant improvements over previous architectures.

We present new supernova (SN 1987A) cooling bounds on sub-MeV fermionic dark matter with effective couplings to electrons. These bounds probe the parameter space relevant for direct detection experiments in which dark matter can be absorbed by the target material, showing strong complementarity with indirect searches and constraints from dark matter overproduction. Crucially, our limits exclude the projected sensitivity regions of current and upcoming direct detection experiments. Since these conclusions are a priori not valid for light mediators, we extend our analysis to this case. We show that sub-GeV mediators can be produced resonantly both in supernova cores and in the early Universe, altering the SN 1987A analysis for effective couplings. Still, a combination of supernova cooling constraints and limits from dark matter overproduction excludes the entire parameter space relevant for direct detection in this case.

We introduce two diffusion models and an autoregressive transformer for LHC physics simulations. Bayesian versions allow us to control the networks and capture training uncertainties. After illustrating their different density estimation methods for simple toy models, we discuss their advantages for Z plus jets event generation. While diffusion networks excel through their precision, the transformer scales best with the phase space dimensionality. Given the different training and evaluation speed, we expect LHC physics to benefit from dedicated use cases for normalizing flows, diffusion models, and autoregressive transformers.

The phenomenology of axions and axionlike particles strongly depends on their couplings to Standard Model particles. The focus of this paper is the phenomenology of the unique dimension-six operator respecting the shift symmetry: the axion-Higgs portal. We compare constraints from Higgs physics, flavor-violating and radiative meson decays, bounds from atomic spectroscopy searching for fifth forces, and astrophysical observables. In contrast to the QCD axion, axions interacting through the axion-Higgs portal are stable and can provide a dark matter candidate for any axion mass. We derive the parameter space for which freeze-out and freeze-in production, as well as the misalignment mechanism, can reproduce the observed relic abundance and compare the results with the phenomenological constraints. For comparison, we also derive Higgs, flavor, and spectroscopy constraints, and the parameter space for which the scalar Higgs portal without derivative interactions can explain dark matter.

The phenomenology of axions and axion-like particles strongly depends on their couplings to Standard Model particles. The focus of this paper is the phenomenology of the unique dimension six operator respecting the shift symmetry: the axion-Higgs portal. We compare constraints from Higgs physics, flavor violating and radiative meson decays, bounds from atomic spectroscopy searching for fifth forces and astrophysical observables. In contrast to the QCD axion, axions interacting through the axion-Higgs portal are stable and can provide a dark matter candidate for any axion mass. We derive the parameter space for which freeze-out and freeze-in production as well as the misalignment mechanism can reproduce the observed relic abundance and compare the results with the phenomenological constraints. For comparison we also derive Higgs, flavor and spectroscopy constraints and the parameter space for which the scalar Higgs portal without derivative interactions can explain dark matter.